About this ebook
Modern robotics, drones, and autonomous systems depend on combining imperfect data from IMUs, GPS, cameras, LiDAR, and other sensors. Yet most resources on sensor fusion are either too theoretical or too fragmented to apply in real projects.
This book bridges that gap.
Designed for working engineers and advanced developers, it takes a hands-on approach to sensor fusion — starting with intuitive concepts and progressing toward production-ready systems.
Inside This Book You Will Learn How To
Understand and model real sensor noise and failure modes
Build Kalman Filters from scratch for linear systems
Apply Extended and Unscented Kalman Filters to nonlinear problems
Implement complementary filters for embedded systems
Work with real sensors including IMU, GNSS, cameras, and LiDAR
Design multi-sensor fusion architectures with fault detection
Transition from theory to deployable pipelines using ROS
Each concept is presented with:
clear explanations
practical engineering insights
fully runnable Python and C++ examples
You will not only understand how sensor fusion works — you will build systems that actually run, debug them when they fail, and improve them for real-world performance.
By the end of this book, you will have a solid foundation in estimation techniques and the confidence to design robust sensor fusion pipelines for:
robotics
autonomous vehicles
drones
embedded systems
intelligent machines
This is not a purely academic textbook.
It is a field guide for engineers who need results.
